Inscripta, Inc.

Matthew David Estes

Phillip Belgrader

Andrew Garst

Don Masquelier

Bruce Chabansky

Jorge Bernate

Richard Fox

Miles Gander

Charles D Johnson

Krishna Yerramsetty

Paul Hardenbol

Thomas Hraha

Isaac David Wagner

Andrea Halweg-Edwards

Shea Lambert

Chad Douglas Sanada

Disclosed systems and methods relate to predicting the relative representation of genomic variants in an edited cell population, based on the editing cassette design representation in an editing cassette design library used to generate the edited cell population. A library of editing cassette designs is generated, and a feature vector, or sequence embedding, is developed for each design using natural language processing techniques. The feature vector may be based upon sequence attributes and editing kinetics of each cassette design as well as attributes that describe the library context. Features may include sequence embeddings generated from a neural network, linguistic-type distances, and statistical distance summaries thereof. The feature vectors are classified using one or more machine learning models, and the classified feature vectors are used to predict the representation of each design an edited cell population.

Methods and systems for modeling of design representation in a library of editing cassettes

The present disclosure provides instruments, modules and methods for improved detection of edited cells following nucleic acid-guided nuclease genome editing. The disclosure provides improved automated instruments that perform methods&#x2014;including high throughput methods&#x2014;for screening cells that have been subjected to editing and identifying cells that have been properly edited.

Instruments, modules, and methods for improved detection of edited sequences in live cells

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Livermore, CA, United States of America

Matthew David Estes